[unreadable] Within the backdrop of a cost-conscious healthcare system, the need for providing quantitative relationships between the abundance of preoperative imaging information and the physical patient is clear. This is particularly important within the neurosurgical context where there is a distinct lack of anatomical landmarks. Developing a cost-effective method to improve a neurosurgeon's recognition of cortical brain structures during surgery as well as dynamically tracking these regions intraoperatively would be an important contribution to image-guided procedures. Laser range scanning (LRS) is a candidate technology that has the advantage of providing a wealth of intraoperative data with minimal space requirements. The work proposed herein involves a clinically translatable use of LRS technology that could have direct impact on surgical therapy for patients afflicted with brain tumors. With current technology, neurosurgeons often develop a surgical treatment plan by studying the patient's MR tomogram as a segmented reconstructed gray-scale encoded volume rendering. By visualizing the segmented brain in its three-dimensional state with the MR gray-scale providing anatomical landmarks, the surgeon can identify sensitive regions which may be important in the delivery of therapy. For example, gyri that are associated with primary, motor, somesthetic, auditory and visual functions can be identified usually on these renderings or with the assistance of cortical stimulation and/or functional magnetic resonance (MR) imaging. Unfortunately, these regions can often be difficult to recognize intraoperatively due to the lack of landmark recognition within the surgeon's field of view (FOV), i.e. the intraoperative presentation of the cortical surface. One fundamental hypothesis encompassing this proposed research is that textured (texture is from a digital image of the FOV) laser-range scan (tLRS) data can be used to correlate preoperative image data to the surgeon's field of view using a multi-modal registration approach. Specifically, the aims within this proposal are focused at: (1) enhancing surgical guidance by registering textured point clouds to the MR tomogram, (2) providing new anatomical cues to the surgeon, and (3) measuring cortical brain shift during surgical procedures. Although these enhancements are critical for correlating the cortical surface to its MR counterpart, the need to characterize subsurface tissue motion is equally important. As a result, this proposal outlines a strategy to directly address the use of tLRS data as a means to predict subsurface shift within patients undergoing a neurosurgical intervention. In summary, if image-guided procedures are to advance surgical therapy, the next evolution of these systems will require methods to account for soft tissue deformation. The tLRS studies proposed herein represent a clinically translatable strategy for documenting brain shift which could be pivotal in developing affordable image-guided platforms capable of compensating for soft tissue deformation. [unreadable] [unreadable]